Python Project – Automatic License Number Plate Recognition using Deep Learning
Automatic number-plate recognition (ANPR; see also other names below) is a technology that uses optical character recognition on images to read vehicle registration plates to create vehicle location datingyougirl.com can use existing closed-circuit television, road-rule enforcement cameras, or cameras specifically designed for the datingyougirl.com is used by police forces around the world for law enforcement. Deep Learning Project – Automatic License Number Plate Detection and Recognition. This project aims to recognize license number plates. In order to detect license number plates, we will use OpenCV to identify number plates and python pytesseract to extract characters and digits from the number plates.
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Automatic number-plate recognition ANPR ; see also other names below is a technology that uses optical character recognition on images to read vehicle registration plates to create vehicle location data. It can use existing closed-circuit television , road-rule enforcement cameras , or cameras specifically designed for the task.
ANPR is used by police forces around the world for law enforcement purposes, including to check if a vehicle is registered or licensed. It is also used for electronic toll collection on pay-per-use roads and as a method of cataloguing the movements of traffic, for example by highways agencies. Automatic number-plate recognition can be used to store the images captured by the cameras as well as the text from the license plate, with some configurable to store a photograph of the driver.
Systems commonly use infrared lighting to allow the camera to take the picture at any time of day or night. Privacy issues have caused concerns about ANPR, such as government tracking citizens' movements, misidentification, high error rates, and increased government spending. Critics have described it as a form of mass surveillance. Early trial systems were deployed on the A1 road and at the Dartford Tunnel. The first arrest through detection of a stolen car was made in The collection of ANPR data for future use i.
The software aspect of the system runs on standard home computer hardware and can be linked to other applications or databases. It first uses a series of image manipulation techniques to detect, normalize and enhance the image of the number plate, and then optical character recognition OCR to extract the alphanumerics of the license plate.
ANPR systems are generally deployed in one of two basic approaches: one allows for the entire process to be performed at the lane location in real-time, and the other transmits all the images from many lanes to a remote computer location and performs the OCR process there at some later point in time.
When done at the lane site, the information captured of the plate alphanumeric, date-time, lane identification, and any other information required is completed in approximately milliseconds. In the other arrangement, there are typically large numbers of PCs used in a server farm to handle high workloads, such as those found in the London congestion charge project.
Often in such systems, there is a requirement to forward images to the remote server, and this can require larger bandwidth transmission media. When Dutch vehicle registration plates switched to a different style in , one of the changes made was to the font , introducing small gaps in some letters such as P and R to make them more distinct and therefore more legible to such systems. Some license plate arrangements use variations in font sizes and positioning—ANPR systems must be able to cope with such differences in order to be truly effective.
More complicated systems can cope with international variants, though many programs are individually tailored to each country. The cameras used can be existing road-rule enforcement or closed-circuit television cameras, as well as mobile units, which are usually attached to vehicles.
Some systems use infrared cameras to take a clearer image of the plates. During the s, significant advances in technology took automatic number-plate recognition ANPR systems from limited expensive, hard to set up, fixed based applications to simple "point and shoot" mobile ones. This was made possible by the creation of software that ran on cheaper PC based, non-specialist hardware that also no longer needed to be given the pre-defined angles, direction, size and speed in which the plates would be passing the camera's field of view.
Further scaled-down components at more cost-effective price points led to a record number of deployments by law enforcement agencies around the world. Smaller cameras with the ability to read license plates at higher speeds, along with smaller, more durable processors that fit in the trunks of police vehicles, allowed law enforcement officers to patrol daily with the benefit of license plate reading in real time, when they can interdict immediately.
Despite their effectiveness, there are noteworthy challenges related with mobile ANPRs. This equipment must also be very efficient since the power source is the vehicle battery, and equipment must be small to minimize the space it requires. Relative speed is only one issue that affects the camera's ability to actually read a license plate.
Algorithms must be able to compensate for all the variables that can affect the ANPR's ability to produce an accurate read, such as time of day, weather and angles between the cameras and the license plates. A system's illumination wavelengths can also have a direct impact on the resolution and accuracy of a read in these conditions.
Installing ANPR cameras on law enforcement in the vehicles requires careful consideration of the juxtaposition of the cameras to the license plates they are to read. Using the right number of cameras and positioning them accurately for optimal results can prove challenging, given the various missions and environments at hand.
Highway patrol requires forward-looking cameras that span multiple lanes and are able to read license plates at very high speeds. City patrol needs shorter range, lower focal length cameras for capturing plates on parked cars. Parking lots with perpendicularly parked cars often require a specialized camera with a very short focal length.
Most technically advanced systems are flexible and can be configured with a number of cameras ranging from one to four which can easily be repositioned as needed.
States with rear-only license plates have an additional challenge since a forward-looking camera is ineffective with oncoming traffic. In this case one camera may be turned backwards. There are seven primary algorithms that the software requires for identifying a license plate:. The complexity of each of these subsections of the program determines the accuracy of the system. During the third phase normalization , some systems use edge detection techniques to increase the picture difference between the letters and the plate backing.
A median filter may also be used to reduce the visual noise on the image. There are a number of possible difficulties that the software must be able to cope with. These include:. While some of these problems can be corrected within the software, it is primarily left to the hardware side of the system to work out solutions to these difficulties. Increasing the height of the camera may avoid problems with objects such as other vehicles obscuring the plate but introduces and increases other problems, such as adjusting for the increased skew of the plate.
On some cars, tow bars may obscure one or two characters of the license plate. Bikes on bike racks can also obscure the number plate, though in some countries and jurisdictions, such as Victoria, Australia , "bike plates" are supposed to be fitted. Some small-scale systems allow for some errors in the license plate. When used for giving specific vehicles access to a barricaded area, the decision may be made to have an acceptable error rate of one character.
This is because the likelihood of an unauthorized car having such a similar license plate is seen as quite small. However, this level of inaccuracy would not be acceptable in most applications of an ANPR system.
At the front end of any ANPR system is the imaging hardware which captures the image of the license plates. The initial image capture forms a critically important part of the ANPR system which, in accordance to the garbage in, garbage out principle of computing, will often determine the overall performance.
License plate capture is typically performed by specialized cameras designed specifically for the task, although new [ when? Factors which pose difficulty for license plate imaging cameras include the speed of the vehicles being recorded, varying level of ambient light, headlight glare and harsh environmental conditions.
Most dedicated license plate capture cameras will incorporate infrared illumination in order to solve the problems of lighting and plate reflectivity. Many countries now use license plates that are retroreflective.
In some countries, the characters on the plate are not reflective, giving a high level of contrast with the reflective background in any lighting conditions. A camera that makes use of active infrared imaging with a normal colour filter over the lens and an infrared illuminator next to it benefits greatly from this as the infrared waves are reflected back from the plate. This is only possible on dedicated ANPR cameras, however, and so cameras used for other purposes must rely more heavily on the software capabilities.
Further, when a full-colour image is required as well as use of the ANPR-retrieved details, it is necessary to have one infrared-enabled camera and one normal colour camera working together. It is also important that the camera use a global shutter, as opposed to rolling shutter , to assure that the taken images are distortion-free. Because the car is moving, slower shutter speeds could result in an image which is too blurred to read using the OCR software, especially if the camera is much higher up than the vehicle.
In slow-moving traffic, or when the camera is at a lower level and the vehicle is at an angle approaching the camera, the shutter speed does not need to be so fast. To maximize the chances of effective license plate capture, installers should carefully consider the positioning of the camera relative to the target capture area.
Exceeding threshold angles of incidence between camera lens and license plate will greatly reduce the probability of obtaining usable images due to distortion. Manufacturers have developed tools to help eliminate errors from the physical installation of license plate capture cameras. The city of Mechelen uses an ANPR system since September to scan all cars crossing the city limits inbound and outbound.
Cars listed on ' black lists ' no insurance, stolen, etc. As of early , 1 million cars per week are automatically checked in this way. Federal, provincial, and municipal police services across Canada use automatic licence plate recognition software; they are also used on certain toll routes and by parking enforcement agencies. Laws governing usage of information thus obtained use of such devices are mandated through various provincial privacy acts.
The technique is tested by the Danish police. It has been in permanent use since mid These together with a further fixed cameras is to enable a levy of an eco tax on lorries over 3. The system is currently being opposed and whilst they may be collecting data on vehicles passing the cameras, no eco tax is being charged. On 11 March , the Federal Constitutional Court of Germany ruled that some areas of the laws permitting the use of automated number plate recognition systems in Germany violated the right to privacy.
In a state consortium was formed among the Hungarian Ministry of Interior, the National Police Headquarters and the Central Commission of Public Administration and Electronic Services with the aim to install and operate a unified intelligent transportation system ITS with nationwide coverage by the end of Since all the data points are connected to a centrally located ITS, each member of the consortium is able to separately utilize its range of administrative and enforcement activities, such as remote vehicle registration and insurance verification, speed, lane and traffic light enforcement and wanted or stolen vehicle interception among others.
Several Hungarian auxiliary police units also use a system called Matrix Police  in cooperation with the police. It consists of a portable computer equipped with a web camera that scans the stolen car database using automatic number-plate recognition. The system is installed on the dashboard of selected patrol vehicles PDA -based hand-held versions also exist and is mainly used to control the license plate of parking cars.
As the Auxiliary Police do not have the authority to order moving vehicles to stop, if a stolen car is found, the formal police is informed. Vehicle registration plates in Saudi Arabia use white background, but several vehicle types may have a different background. There are only 17 Arabic letters used on the registration plates.
Some plates use both Eastern Arabic numerals and the 'Western Arabic' equivalents. The technique is tested by the Swedish Police Authority at nine different locations in Sweden. Now the system has been widened to network all the registration number cameras together, and enforcing average speed over preset distances. The project of system integration «OLLI Technology» and the Ministry of Internal Affairs of Ukraine Department of State Traffic Inspection STI experiments on the introduction of a modern technical complex which is capable to locate stolen cars, drivers deprived of driving licenses and other problem cars in real time.
The Ukrainian complex "Video control"  working by a principle of video fixing of the car with recognition of license plates with check under data base. The Home Office states the purpose of automatic number-plate recognition in the United Kingdom is to help detect, deter and disrupt criminality including tackling organised crime groups and terrorists.
These records are stored for up to two years in the National ANPR Data Centre, which can be accessed, analysed and used as evidence as part of investigations by UK law enforcement agencies. In , the UK Parliament enacted the Protection of Freedoms Act which includes several provisions related to controlling and restricting the collection, storage, retention, and use of information about individuals.
Under this Act, the Home Office published a code of practice in for the use of surveillance cameras, including ANPR, by government and law enforcement agencies. The aim of the code is to help ensure their use is "characterised as surveillance by consent, and such consent on the part of the community must be informed consent and not assumed by a system operator.
Surveillance by consent should be regarded as analogous to policing by consent. With the widespread implementation of this technology, many U.